Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.

ABSTRACT: Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and

SUBMITTER: Berntsen J 

PROVIDER: S-EPMC8809568 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature